What question did this study set out to answer?

The study investigates how AI transforms tax auditing and its implications for equity and governance.

May 14, 2026Open Access

From Predictive Accuracy to Algorithmic Justice: Mapping the Multidimensional Impact of AI in Tax Auditing

Key Points

The study investigates how AI transforms tax auditing and its implications for equity and governance.
Conducted a PRISMA-compliant systematic literature review of articles from 2015 to 2025.
Utilized textometric analysis with the IRAMUTEQ software to analyze literature.
Developed the AI-Driven Tax Audit Model (ATAM) for tax authorities.
Highlighted advancements in tax fraud prediction and financial risk assessment through deep learning (e.g., neural networks).
Identified ethical and psychological challenges regarding taxpayer equity and trust.
Proposed a framework balancing algorithmic efficiency with vertical equity and explainability.

Abstract

This study examines the transformative impact of artificial intelligence (AI) on tax auditing through a PRISMA-compliant systematic literature review and textometric analysis. By analyzing literature published between 2015 and 2025 using IRAMUTEQ, we uncover a nuanced perspective on AI’s evolving role. The results reveal a scholarly discourse highlighting significant advances in tax fraud prediction and financial risk assessment via deep learning and neural networks. This technological shift extends beyond operational efficiency to broader macroeconomic governance, simultaneously raising challenges regarding taxpayer equity and trust. Our findings underscore a transition in academic focus from purely technical applications to the ethical and psychological dimensions of AI. Finally, we propose the AI-Driven Tax Audit Model (ATAM), a framework designed to guide tax authorities in integrating these technologies by balancing algorithmic efficiency and financial risk mitigation with vertical equity and explainability.

Read Full Paperexternally

Perguntar à IA

Bookmark

View Full Paper